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Nonlinear optimisation using directional step lengths based on RPROP

Kotsialos, A.

Authors

A. Kotsialos



Abstract

A search method based on the backpropagation rule commonly used for training neural networks is proposed here for the optimisation of smooth nonlinear functions. The use of the Resilient backPROPagation (RPROP) heuristic rule for local minimisation is described. The details of employing the directional step length determined by RPROP along with a simple restarting scheme are provided. In the approach proposed here direct use of the directional step determined by the heuristic without using any line search conditions takes place. The overall algorithm has been tested on a number of benchmark functions found in the literature with very positive results. The test problems’ dimension ranges from 100 to 50,000. The results obtained show that the suggested search direction method results to a highly efficient algorithm suitable for large scale optimisation.

Citation

Kotsialos, A. (2014). Nonlinear optimisation using directional step lengths based on RPROP. Optimization Letters, 8(4), 1401-1415. https://doi.org/10.1007/s11590-013-0668-8

Journal Article Type Article
Acceptance Date Jun 15, 2013
Publication Date Apr 1, 2014
Deposit Date Sep 16, 2013
Journal Optimization Letters
Print ISSN 1862-4472
Electronic ISSN 1862-4480
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 8
Issue 4
Pages 1401-1415
DOI https://doi.org/10.1007/s11590-013-0668-8
Keywords Nonlinear optimization, Search methods, Backpropagation methods, RPROP.

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